Train Operational Plan Optimization for Urban Rail Transit Lines Considering Circulation Balance

Abstract

The passenger demand of urban rail transit (URT) lines often present asymmetric tidal time-varying characteristics. To match the demand fluctuation, the train operational plan (TOP) generally has asymmetric bi-directional frequency/headway setting and imbalanced circulation, leading to high operation cost. This paper incorporates circulation balance into TOP optimization to balance the bi-directional arrival, departure, circulation, and resource utilization, and reduce the overall operation cost. Based on time-varying section demand and predetermined service level, bi-directional stepped maximum headway functions are collaboratively constructed, and then the circulation process is described by the trip flow circulation network that is formulated as a cost-oriented integer linear programming model. Using the optimized frequency setting, the final TOP is obtained by a two-stage approach to successively solve the schedule and rolling stock circulations at terminals. The case study based on an URT line in Shenzhen indicates that the proposed approach can not only ensure the required service level for travel demand, but also improve the efficiency of circulation and utilization, and effectively reduce the overall operation cost. The proposed approach provides an effective technique to keep balanced, stable and sustainable operation for URT lines.

Optimization Algorithm for Urban Rail Transit Operation Scheduling based on Linear Programming

“…Zhao S et al, in order to adapt to the asymmetric tidal time-varying characteristics of URT line passenger demand and reduce the cost of train operation, studied the introduction of cycle balance into the top-level optimization, and built a low cost oriented integer linear programming model. The results of a case study based on even a certain URT route show that this method not only meets the service level of travel needs, but also improves the efficiency of circulation and utilization [5]. Li X et al studied and analyzed the overall travel characteristics of passengers after the URT operation in order to study the passengers' choice of URT and public transportation after the URT operation, and then used the random forest algorithm to establish the passenger travel mode selection model after the URT is put into use.…”

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

“…Some authors modelled more than one aspect, for example routes and frequencies setting [194], routes and vehicle scheduling of a customised bus service [89], routes and timetabling [187], frequencies setting and timetable development [193], and service area, frequencies, and vehicle scheduling [164]; Nesheli et al [127] focused on real-time operation aspects of bus systems, as vehicle holding time and boarding limit at stops, and also deciding if the vehicle could skip certain stops. Finally, for rail systems, studies focused on determining train timetables and rolling stock schedule [63], a conflict-free timetable for all trains running on a railway, considering multiplatform stations [172], frequencies setting and operational aspects (stop scheme and operation time) [93], rail scheduling and fleet management [100], and a transit-based evacuation plan for rail transit line emergencies [71].…”

“…168, 170, 171, 173-177, 180, 181, 183-186, 188, 190, 192-199] Considering the perspective of passengers alone, studies sought to minimise uncovered demand [174], distances in the network [156], and passengers' travel time [68,93,127,190], and maximise accessibility [147] and direct transfers without waiting [127]. For the operators perspective alone, studies aimed at minimising costs or maximising revenue [84,98,100,101,152,154,160,182,185,192], minimising travelled distance or vehicle mileage [72-74, 92, 175, 187, 191], minimising workload [73] and rebalancing amount [192], maximising covered demand [73,176], rebalancing utility [191], the total social net benefit [47], and the average passenger flow [173].…”

Metro Train Stopping Scheme Decision Based on Multisource Data in Express-Local Train Mode

“…Based on IC card data, Zheng and Jin [13] proposed a new subway operation plan formulation method and obtained a departure plan that was closer to the passenger fow demand. Zhao et al [14] established an integer linear programming model based on time-varying section requirements and predetermined service levels and used the two-stage method to solve the fnal train running plan. Based on AFC data, Yang et al [15] proposed comprehensive optimization to formulate route plans and schedules.…”